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Gideon Dresdner

6 accepted papers

2024

Precipitation Downscaling with Spatiotemporal Video Diffusion

Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy J McGibbon, Christopher S. Bretherton +1
NeurIPS 2024poster

In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statist…

Cited by 5Source ↗PDF ↗Scholar ↗
2022

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
AISTATS 2022poster

We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully increasing the batch size over the course of t…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2021

Boosting Variational Inference With Locally Adaptive Step-Sizes

Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
IJCAI 2021poster

Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. Th…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2021

Neighborhood Contrastive Learning Applied to Online Patient Monitoring

Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
ICML 2021spotlight

Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated p…

Cited by 50Source ↗PDF ↗code ↗Scholar ↗
2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund +1
ICML 2020poster

We propose a novel Stochastic Frank-Wolfe (a. k. a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. This class of problems includes empirical risk minimization with sparse, low-rank, or other structured constraints. The p…

Cited by 41Source ↗PDF ↗code ↗Scholar ↗
2018

Boosting Black Box Variational Inference

Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Raetsch
NeurIPS 2018spotlight

Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by choosing a relatively simple variational approximation. Borrowing ideas from the classic boosting framework, recent appr…

Cited by 39Source ↗PDF ↗code ↗Scholar ↗